Аннотация
Automated segmentation of pavement distress is critical for efficient maintenance and asset management of road networks. This study presents a Mask R-CNN–based framework for pixel-level detection and delineation of four distress categories longitudinal cracks, transverse cracks, alligator cracks, and potholes using the publicly available RDD2020 dataset. The proposed architecture integrates a ResNet-101 backbone for multi-scale feature extraction, ROI Align for high-fidelity region pooling, and specialized mask and bounding-box heads for simultaneous classification and regression. The model was trained for 100 epochs with extensive data augmentation and achieved a mean Intersection over Union (mIoU) of 0.78 and a mean Average Precision (mAP) of 0.85 across all classes on a held-out test split. Notably, pothole segmentation yielded an IoU above 0.88, while fragmented alligator cracks attained an IoU of 0.69, reflecting the challenges of fine-grained crack morphology. Rapid convergence of training and validation curves indicates strong generalization with minimal overfitting, even under varying lighting, surface textures, and environmental conditions. The end-to-end mask prediction enables precise quantification of damage extents, facilitating automated road health mapping and prioritization of repairs. Future enhancements will explore attention mechanisms for micro-crack sensitivity, semi-supervised learning to leverage unlabeled imagery, and fusion with three-dimensional sensing modalities for improved robustness in complex scenarios.